Temperature at fine spatial scales is an important driver of nest site selection for many avian species during the breeding season and can influence nest success. Sagebrush (Artemisia spp.) communities have areas with high levels of vegetation heterogeneity and high thermal variation; however, fire removes vegetation that provides protection from predators and extreme environmental conditions. To examine  the influence of microclimates on Greater Sage-Grouse (Centrocercus urophasianus) nest site selection and nest success in a fire affected landscape, we measured black bulb temperature (T_bb) and vegetation attributes (e.g. visual obstruction) at 3 spatial scales (i.e. nest bowl, microsite, and landscape) in unburned and burned areas. Nest bowls exhibited greater buffering of T_bb than both nearby microsites and the broader landscape. Notably, nest bowls were warmer in cold temperatures, and cooler in hot temperatures, than nearby microsites and the broader landscape, regardless of burn stage. Nest survival (NS) was higher for nests in unburned areas compared to nests in burned areas (unburned NS = 0.43, 95% CI: 0.33 to 0.54; burned NS = 0.24, 95% CI: 0.10 to 0.46). Amount of bare ground was negatively associated with nest survival, but effects diminished as the amount of bare ground reached very low levels. Shrub height and visual obstruction were positively associated with nest survival during the entire study period whereas, minimum T_bb had a weaker effect. Our findings demonstrate that thermoregulatory selection by Greater Sage-Grouse at nest sites had marginal effects on their nest survival. However, given that increases in vegetation structure (e.g. shrub height) provide thermal refuge and increase nest survival, vegetation remnants or regeneration in a post-fire landscape could be critical to Greater Sage-Grouse nesting ecology.

We attached 22-g or 30-g ARGOS/GPS Solar PTTs (Microwave Telemetry, Columbia, Maryland, USA) to adult and yearling female Greater Sage-grouse using a rump-mount technique (Rappole and Tipton 1991). GPS-PTT units were programmed to collect 4–6 locations per day for each individual during the nesting season (1 April to 31 May), and this location frequency allowed us to monitor nest status remotely after they were located. We considered a female to be nesting if GPS locations remained stationary for >18 hr during the nesting season.

We used stainless steel spheres (15.24 cm-diameter, 20 gauge, 304 alloy) painted matte black (i.e. black bulb) with 1 temperature sensor (Onset Corporation, Bourn, Massachusetts, USA) suspended in the center, to measure black bulb temperature (T_bb). We measured T_bb at 3 spatial scales: nest bowl, nest microsite (6 m around nest), and the surrounding landscape within unburned and burned areas. An array consisting of 4 black bulbs attached to a HOBO 12U (Onset Corporation, Bourn, Massachusetts, USA) data logger was deployed at each nest bowl and associated microsite (i.e. nest array) and random landscape point (i.e. landscape array). At each nest site, we placed 1 black bulb in the nest bowl; and 3 black bulbs at 2 m, 4 m, and 6 m in random cardinal directions from the nest bowl (i.e. microsite). We recorded T_bb at 15-min intervals for a 24-hr period.

We measured vegetation cover and height, and visual obstruction along two perpendicular 10-m transects centered on each nest bowl or landscape point (Connelly et al. 2003). We estimated percent cover of vegetation by dropping a pin flag at 0.5-m intervals along transects where we then recorded the number of shrubs, perennial grasses, forbs, cheatgrass, and bare ground that intersected the pin (i.e. line-point intercept method; Canfield 1941). The number of times a specific vegetation type was intersected by a pin divided by the total number of points along each transect was the estimate of percent cover. We measured the effective height (i.e. tallest vegetation that concealed 50% of a 2.5-cm-diameter pole) of the tallest shrub, perennial grass, and forb within a 30-cm cylinder centered on the transect at 1-m intervals (Musil 2011). We measured visual obstruction by placing a 150-cm pole that was taped in 10 cm increments at 0 m, 5 m, and 10 m along each transect (i.e., Robel Pole Method; Robel et al. 1970).

Thermal Nest Selection Analysis

Requires R Project for Statistical Consulting (free software environment available at: https://www.r-project.org/).

Figure 1, and data for Tables 2 and 3 in the manuscript can be attained by running lines 1 – 403 in the R script titled CRA_2020_Sagegrouse_Thermal_Nest.R. *User must set the working directory (setwd) to their own files (ex. Line 10) and have the csv file titled nest_temp_2016-2018_final.csv in their file.

Figure 2 can be attained by running lines 407 – 548 403 in the R script titled CRA_2020_Sagegrouse_Thermal_Nest.R. *User must set the working directory (setwd) to their own files (ex. Line 10) and have the csv file titled nest_temp_2016-2018_final_naremoved.csv in their file.

Thermal Nest Survival Analysis

Requires Program MARK (free software available at: http://www.phidot.org/software/mark/).

Requires DBF, FTP, and inp files.

The user should be able to use the following path in Program Mark to open each of the files associated with each “Stage” of the analysis: File > Open > XXXX.DBF.

Example: File > Open > TheCondor_Stage1.DBF